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Record W2769294420 · doi:10.5719/hgeo.2017.112.4

Spatiality of ethnic identity and construction of sociopolitical interaction in South Sudan

2017· article· en· W2769294420 on OpenAlexaff
Kon K. Madut

Bibliographic record

VenueHUMAN GEOGRAPHIES – Journal of Studies and Research in Human Geography · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicPolitical Conflict and Governance
Canadian institutionsUniversity of OttawaWilfrid Laurier University
Fundersnot available
KeywordsEthnic groupPoliticsIndependence (probability theory)Identity (music)Meaning (existential)Gender studiesSociologySocial constructionismSocial relationPolitical scienceSocial scienceAnthropologyAestheticsPsychologyLaw

Abstract

fetched live from OpenAlex

This article explores the complexity of the spatial construction of ethnicity, identity, and sociopolitical interaction among South Sudanese ethnic groups. The article focuses on the interplay between social interaction and the construction of ethnic identity as they affect the notion of human interaction and welfare. The narratives are based on the political sociology of South Sudan after its independence from Sudan and challenges endured in the process of sociopolitical transformation towards the reconstruction of national identity and peaceful coexistence. This discourse gives meaning to visible and invisible ethno-cultural constructions that shaped the norms of social and political interactions among various ethnic groups in the country. The analysis concluded that South Sudan society is socially, politically, and culturally constructed along ethnicized communities with variant perceptions of group and regional identities based on both primordial ties and instrumentalists' perceptions. These unique characteristics of spaces and construction of social structure has created multifaceted challenges in the process of social, economic and political reconstruction after the independent of South Sudan in July 2011.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.029
Threshold uncertainty score0.994

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.011
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.240
GPT teacher head0.512
Teacher spread0.272 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations4
Published2017
Admission routes1
Has abstractyes

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